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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Create Research Report

create_report
Idempotent

Synchronously generate a research report and persist it under the caller's authorship. Two subtypes:

• reverse_dcf — solves the stage-1 free-cash-flow growth rate the market price implies, with a 5×5 sensitivity grid across WACC × terminal-growth assumptions. Returns full markdown + structured JSON + every numerical claim's citation chain to the originating SEC accession.

• thesis — snapshot a saved thesis (via save_thesis) as a frozen narrative report with at-a-glance table, author notes, anchor fundamentals (latest annual), and lineage to the source filing. Later edits to the thesis do NOT propagate — generate a new report to capture new state.

Tier: sample tier rejected — reports are per-author state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNoOptional human-supplied title; auto-generated when omitted.
paramsNoReverse-DCF parameters — required for report_type=reverse_dcf.
tickerNoUS-listed ticker — required for report_type=reverse_dcf. Case-insensitive.
thesis_idNoId of a saved thesis owned by the caller — required for report_type=thesis.
report_typeYesSubtype. `reverse_dcf` requires ticker + params; `thesis` requires thesis_id (from save_thesis / list_theses).
idempotency_keyNoOptional key for at-most-once semantics. Same key from the same user always yields the same report id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
reportYes
markdownYes
sectionsYes
citationsYes
structuredYes

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate idempotentHint=true and non-destructive. Description adds that generation is synchronous, persisted under caller's authorship, idempotency key provides at-most-once semantics, and output includes markdown, JSON, and citation chains. No contradictions; adds useful context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with bullet points for subtypes, front-loaded with main purpose, and each sentence adds value. The 'Tier: sample tier rejected' line is slightly unclear but not detrimental. Concise enough for a tool with two complex subtypes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 params, nested objects, output schema exists), the description covers subtypes, output format, and idempotency. It lacks some error handling details but is sufficiently complete for an agent to accurately select and invoke the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds value by grouping parameters per subtype, explaining conditional requirements (e.g., params only for reverse_dcf), and noting that idempotency_key ensures idempotency. This enhances understanding beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool creates a research report and persists it under the caller's authorship. It distinguishes two subtypes (reverse_dcf and thesis) with specific behaviors, and the purpose is distinct from sibling tools like get_report, update_report, or save_thesis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use each subtype: reverse_dcf requires ticker and params, thesis requires thesis_id from save_thesis. It notes that thesis reports are frozen snapshots that do not update with edits, implying when to generate a new one. Explicit mention of alternatives could be stronger, but it provides clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.

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